Best Deep Learning cheat sheets in May, 2019

Best Deep Learning cheat sheets

Are you looking for Top and Best Quality Deep learning cheat sheets, loaded up with valuable then you have come to the right place.

 

Below are the “VIP cheat sheets” for Deep Learning Cheat Sheets includes topics as shown below:

 

Convolutional Neural NetworksCheck Here Cheat Sheet Here

 

In deep learning, a convolutional neural network is a class of deep neural networks, most commonly applied to analyzing visual imagery. CNNs use a variation of multilayer perceptrons designed to require minimal preprocessing.

 

 

Recurrent Neural Networks – Check Here Cheat Sheet Here

 

A recurrent neural network is a class of artificial neural network where connections between nodes form a directed graph along a sequence. This allows it to exhibit temporal dynamic behavior for a time sequence.

Unlike feedforward neural networks, RNNs can use their internal state to process sequences of inputs.

 

Hyperparameter tuning – Check Here Cheat Sheet Here

 

In machine learning, a hyperparameter is a parameter whose value is set before the learning process begins. By contrast, the values of other parameters are derived via training.

Different model training algorithms require different hyperparameters, some simple algorithms require none.

 

Object recognition – Check Here Cheat Sheet Here

 

An approach to building an object detection is to first build a classifier that can classify closely cropped images of an object.

 

 

Regularization – Check Here Cheat Sheet Here

 

Regularization is a key component in preventing overfitting. Also, some techniques of regularization can be used to reduce model capacity while maintaining accuracy, for example, to drive some of the parameters to zero.

This might be desirable for reducing model size or driving down cost of evaluation in mobile environment where processor power is constrained. 

 

Tips and tricks – Check Here Cheat Sheet Here

 

Deep learning models usually need a lot of data to be properly trained. It is often useful to get more data from the existing ones using data augmentation techniques. The main ones are summed up in the cheat sheet.

 

 

FINAL WORDS

Above repository goes for summing up in a similar place all the imperative ideas that are shrouded in Stanford’s CS 230 Deep Learning course, and include:

Cheatsheets enumerating everything about convolutional neural systems,recurrent neural networks, as well as the DL tips and traps to have at the top of the priority list when preparing a deep learning model.

All components of the above joined in an ultimate arrangement of ideas, to have with you consistently.

 

 

Related:

Best Machine Learning Cheat Sheet

 

 

 

Credits: Afshine Amidi (Ecole Centrale Paris, MIT) graduate student at Stanford and Shervine Amidi (Ecole Centrale Paris, Stanford University) of MIT and Uber are based on the materials from Stanford’s CS 230 (Github repo)

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